The U.S. Bureau of Alcohol, Tobacco, Firearms and Explosives, or ATF, says a cyberattack on one of its systems has been declared a “major incident,” a formal, legally defined classification that prompts a formal notification to lawmakers in Congress.

ATF said in a statement that it’s responding to the cyberattack on a stand-alone system that’s separate from the bureau’s network. An ATF spokesperson told reporters that the targeted computer system contained information such as the “targets of ATF investigations.”

TechCrunch has seen a claim of responsibility by the Qilin ransomware gang on its leak site, but it did not provide evidence for its claim, such as a sample of leaked data. Qilin is known for running a “ransomware-as-a-service” operation, in which it leases its hacking tools to other criminal affiliates for a cut of the profits. The gang has listed media giant Lee Enterprises and U.K. pathology lab giant Synnovis as targets.

Under federal law, “major incidents” include significant cyber incidents that are likely to cause demonstrable harm to U.S. national security or broader U.S. interests. Agencies are required to disclose major incidents to Congress within a week of their discovery.

The ATF joins several government agencies in recent years that have declared major incidents following a breach, including a 2023 ransomware attack on a system used by the U.S. Marshals Service, and a breach of an FBI system earlier this year that exposed phone numbers of targets under surveillance by federal agents. 

The AI race is producing an uncomfortable management gap. Deloitte’s 2026 research found only 21% of enterprises have mature governance for agentic AI, while 74% expect to use agents at least moderately by 2027. Technology is moving faster than the structures responsible for controlling it.

Recent reporting by NBC News of nearly 700 rogue AI agents escaping controlled environments has renewed scrutiny of human oversight and the limits of autonomous systems. The concern isn’t any longer theoretical as autonomous systems gain access to increasingly consequential business environments.

Executive economics are shifting at the same time. A 2026 market data report identifies engineering among the most in-demand fractional functions, with demand for fractional talent up 9% over the previous 90 days. Fractional leadership has spread from marketing and finance into the technology suite specifically because the cost of a full-time CTO has climbed alongside the complexity of the job itself.

Senior technical salaries continue to rise, AI adoption has turned “keeping pace” into a moving target, and boards are asking a version of the same question in growth-stage companies across every sector: Does this organization need a full-time executive, or does it need full-time expertise on a part-time basis?

Daniel Kirichanski, founder of Prime Path Global, argues that this shift represents a new form of technology leadership. His target is the founder or CEO who may assume that the next stage of growth requires a full-time CTO, without considering whether the business needs that role on a permanent basis.

His engagements begin with an onboarding period that can last one to three months, allowing him to understand how the organization operates before setting its technology direction. He approaches the assignment as an executive joining the company, with the fractional structure changing the duration of the commitment rather than the depth of involvement.

Traditional consulting can end with recommendations being handed back to management. Kirichanski’s model keeps the technology leader involved in execution. The commercial structure follows the same philosophy. Kirichanski works through monthly retainers tied to goals rather than hourly billing. A defined objective might involve producing a strategic technology roadmap within an agreed period and reviewing it with the board.

He says, “Senior technology expertise creates value through decisions rather than hours logged. An outcome-based structure gives companies a more direct way to measure the contribution of executive-level technology leadership.”

AI makes the argument more urgent. Deloitte found that 75% of surveyed leaders believe human collaboration with AI agents creates more value than automation alone, while only 5% of organizations reported highly prepared business processes for agents. Kirichanski takes a contrarian view of the technology’s role.

He says, “I would go to the core of AI, and I would argue that it’s not an intelligence. This is still a prediction machine.” For Kirichanski, the practical consequence is a human-in-the-loop model in which AI accelerates work while consequential decisions remain with people.

His approach is already being applied inside a growing online business that lacks a formal engineering organization. Kirichanski is helping establish its technology foundation while introducing AI agents into workflows. The objective, he explains, is to increase operating capacity without automatically increasing headcount.

He states, “We do not replace people with AI; we augment people.” This defines the core of his technology roadmap: automation should free employees from repetitive work so that human effort can move toward work requiring judgment and creative thinking.

Kirichanski believes fractional technology leadership is part of a wider shift toward an economy where specialized executive expertise can be accessed when a company needs it rather than permanently maintained on its payroll. He says, “Fractional work, especially in technology, is the new wave. It’s like a tsunami.”

For founders navigating rapid growth, increasing technology complexity, and an AI landscape evolving at unprecedented speed, Fractional CTO leadership offers a new model for accessing experienced technology leadership. By bringing senior technology executives into the business when their expertise matters most, companies can make better strategic decisions, accelerate AI adoption, strengthen engineering organizations, and build the technology foundations required to scale.

The result is not simply lower executive overhead but greater access to the kind of leadership that can turn technology from an operational necessity into a driver of business growth.

security

US Justice Department investigating the breach

The Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) said it’s responding to a “major” cybersecurity incident shortly after the Qilin ransomware gang posted the US federal law enforcement agency on its leak site.

According to ATF’s statement, the intrusion affected a standalone system that operated separately from its enterprise network. “There is no indication that the incident has affected the ATF enterprise network, the ATF eForms system, or any other ATF system,” the statement said.

ATF, which is housed under the US Department of Justice, said it’s “coordinating closely” with the DOJ to investigate the breach, and “immediately” blocked connections to the affected IT environment upon discovering the incident. 

The statement said the security breach had not affected ATF’s operations and noted that senior Justice Department officials designated the compromise as a “major incident” under federal guidelines.

Shortly before ATF posted its security-incident notice on its website, Russia-linked Qilin ransomware criminals listed the firearms agency on its leak site. The post, seen by The Register and shared on social media, did not say what data Qilin claimed to have stolen, how much, or provide samples to substantiate the claim.

ATF did not immediately respond to our questions, and we will update this story when we receive a response.

Qilin, the notorious crew behind the 2024 attack on pathology provider Synnovis that disrupted NHS services in the UK, was one of the most prolific ransomware gangs in July, according to Comparitech.

The firm, which reviews cybersecurity products and provides data analysis, counted 799 ransomware incidents last month, up from 668 in June. Qilin claimed 125 of those.®

Presented by Gravitee

Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it.

That’s because enterprises don’t deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind. That’s the failure mode that should keep you up at night: a windy, complicated system nobody can see clearly enough to govern. But why do things get so opaque so quickly?

Add a second agent to a system, and you’ve added one connection. Add a tenth, and you haven’t added ten connections, you’ve potentially added dozens, because now any agent might call any other, and each of those calls can trigger a call somewhere else. Complexity doesn’t creep up with agent headcount. It compounds with the number of paths between agents, and nobody’s job is to draw that graph. A support ticket that used to touch one system might now pass through four agents before a human ever lays eyes on it, and every one of those handoffs is a decision point nobody approved.

Most enterprise AI programs stall when the humans responsible for their agents lose the thread. Ask a security team a simple question: which agents can reach which systems, and watch the silence. Ask which agent triggered which downstream action three hops ago. More silence.

The instinct is to treat this like a checklist. Approve the agent. Log the agent. Move on. I’d argue this is the wrong instinct. A checklist checks a single point in time. Complexity runs across a chain, and you can’t govern a chain with a stack of one-time approvals any more than you can call a diet successful because you had a vegetable once.

So where does it actually break down?

Permissions creep first. Somebody builds an agent to summarize support tickets, grants it broad API access because scoping it properly would’ve taken another sprint, and forgets about it. Six months later, that same agent has a path into the payments system. Nobody remembers signing off on that. Nobody did.

And ownership thins out the further the chain runs. Five agents touch one workflow, something breaks at step four, and now you’re asking who’s responsible for a link nobody was ever assigned to own, because the org chart stopped at “deploy the agent” and never got to “name the human who answers for it.”

This is a story about governance infrastructure that hasn’t caught up with how agents actually behave: interconnected, cascading, multiplying faster than the processes built to track them.

Fixing the cluster starts with identity. Every agent needs to exist as its own entity, not a shadow permission borrowed from whoever deployed it. Its own name in the register. Its own scoped authority. A named human sponsor who answers for what it does. That part is necessary.

But it is nowhere near sufficient.

The harder piece is the oversight that holds across the entire chain, not just at each individual link in it. You need to see what an agent did, what it set off downstream, and where that trail ends in real time, not in a report someone pulls together once a quarter. Get agent-level identity right and stop there, and you end up with a filing cabinet full of perfectly documented agents operating inside a system nobody can actually explain.

And oversight by itself only tells you what already happened. Watching a chain isn’t the same as controlling it. Enforcement is the piece most programs skip: the ability to stop an out-of-policy call before it executes, not just log it for someone to find in a review three weeks later. A dashboard that shows you an agent breached its scope five minutes ago is a monitoring tool. A system that stops the breach from happening in the first place is governance. Enterprises serious about agent accountability need both, and most have only built the first.

We’re all running at blazing speed to ensure we’re not the ones left behind in the race we’ve found ourselves in, and we’re all too aware that there’s a cost to slowing down. Every enterprise serious about agentic AI hits the complexity wall eventually. The ones that get past it are the ones who built enough visibility and accountability, so their fleet can keep growing without anyone losing the ability to answer one question: what is this system doing right now, and who’s responsible for it.

But don’t miss the point. Complexity isn’t a reason to pump the brakes. The enterprises getting this right aren’t slowing down. They’re building toward Human-Agent Harmony, where scale and accountability grow together instead of trading off against each other.

The real risk was never a single agent doing exactly what it was built to do. It’s a hundred of them doing exactly that, all at once, interacting in combinations nobody designed for. That kind of multiplication is what keeps enterprise AI stuck running pilots forever instead of running production.

Solve for complexity and autonomy stops being the villain. It starts being the whole point.

Rory Blundell is CEO at Gravitee.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Switching wireless carriers usually starts with a pretty simple goal. You want better value for the money you are already spending. But the calculation can quickly get complicated. Maybe your current phone is reaching the end of its run, or perhaps you upgraded only a few months ago and have no reason to replace it. Then there are monthly service costs, activation fees, taxes, and the question of whether paying less means compromising on the network you rely on every day.

Metro by T-Mobile is addressing those different situations with several ways to save when you switch. Whether you need a new phone, want to keep the device you already have in your pocket, or are thinking about making the jump to iOS, you have the flexibility to choose what adds up for you.

There is less guesswork around the monthly cost too. Metro puts its pricing upfront, with taxes and fees included in the featured plans, so what you see is what you pay each month. That makes it easier to compare your options without wondering how much extra will show up on the bill.

Saving on your monthly service does not necessarily mean leaving a familiar network behind either. Metro runs on T-Mobile, America’s Best Network, which can help address one of the bigger concerns that often comes with switching carriers.

*Based on analysis by Ookla® of Speedtest Intelligence® 1H 2026 data. Metro may be slower than T-Mobile during congestion. Get full terms.

When replacing your old phone does not have to cost a fortune

A battery that barely lasts through the afternoon, a screen that has seen better days, or sluggish performance can eventually turn an upgrade from something you want into something you need. But once you start looking at replacement phones, the cost can quickly become another reason to hold onto your old one for a little longer.

Metro by T-Mobile’s Galaxy A37 5G offer changes that equation. Bring your eligible number to Metro, sign up for the $40 plan, and you can get the Samsung Galaxy A37 5G for free. If you were already preparing to spend hundreds of dollars on a replacement, that is a significant expense you no longer have to factor into the switch. Metro puts the overall value at more than $450 in savings. Taxes and fees are included in the plan price, and there are no activation fees either.

The value is not limited to what you save on the phone upfront. The plan comes with a 5-year price guarantee, which gives you more predictability when thinking about what your service could cost over time. That way, the appeal is not simply getting a new phone when you switch. You also have a clearer picture of what you will be paying after the excitement of the initial deal wears off. But a new phone is only a bargain when you actually need one. If the device already in your pocket still gets you through the day without a problem, keeping it could make more sense than replacing it.

*Just bring your number. Not available if with Metro or T-Mobile in the past 180 days. Port-in is required for the hardware discount. Exclusions apply. Guarantees monthly plan price of on-network talk, text & 5G data.

The BYOD plan makes keeping your phone worthwhile

Maybe you bought your phone fairly recently, or you simply have no reason to replace something that already works for you. Either way, changing carriers should not mean giving up a device you are perfectly happy with just to get a better deal on your service.

Instead, the opportunity to save can come from changing the service around it. Metro by T-Mobile’s $25 BYOD (Bring Your Own Device) offer lets new customers keep their existing phone and sign up online for one line of unlimited 5G for $25 per month. Taxes and fees are included, there are no activation fees, and the plan comes with a 5-year price guarantee.

That makes the transition less about replacing what you already own and more about getting better value from it. You can keep your photos, apps, settings, and everything else right where they are while changing the part of your wireless setup that fits your budget a bit better.

*Unlimited data if using >35GB/mo. May notice reduced speeds due to prioritization.

The iPhone 16e gets a deal of its own

Keeping your current phone can make plenty of sense, but that will not be the plan for everyone. A carrier change can also line up with the moment you are ready to get the phone you have had your eye on.

For shoppers with the iPhone 16e on their radar, Metro by T-Mobile has a separate offer worth considering. The offer is available to eligible customers bringing their existing number to Metro, along with a valid U.S. ID, on the $50 AutoPay plan. You can get the iPhone 16e on Metro by T-Mobile, along with a $100 Virtual Mastercard after the third month and no activation fees. Your first month costs $55, followed by $50 per month with AutoPay.

With three different routes on the table, finding the right one starts with looking at what you actually need from your phone and service. Ultimately, changing your service shouldn’t mean compromising on the phone you want or paying for more than you actually need. Whether you choose a free Android phone, keep your current phone, or move to iOS, Metro by T-Mobile gives you a way to lower your bill without cutting corners. If any of those options sound like a fit, it may be time to see which deal matches what you are looking for.

*Bring your number & ID on $50 AutoPay plan. $100 Virtual Mastercard after 3rd month. 

*1st month is $55 180-day previous active customer exclusions apply. 

As NASA prepares to launch its new Nancy Grace Roman Space Telescope this weekend, Rochester Institute of Technology’s Prof Don Figer examines the telescope’s purpose and its origins in US spycraft.

A version of this article was originally published by The Conversation (CC BY-ND 4.0)

NASA plans to soon launch a new space telescope that will survey a billion galaxies to trace how the universe evolved over time, all courtesy of America’s spy programme and decades of research into detector technology.

The telescope, known as the Nancy Grace Roman Space Telescope, was first developed by the National Reconnaissance Office, before being transferred to NASA in 2012. The intelligence agency no longer needed the hardware for its future missions, so it sent the unused telescope to NASA. The space agency spent over a decade making modifications and aims to launch the telescope into space as early as 30 August 2026.

Once in position, the telescope will take data that astronomers like me hope will help answer some of the field’s most puzzling questions.

Roman’s scientific goals

You can expect a slew of exciting discoveries from the new telescope because it was built to look across the universe and study the three-dimensional distribution of dark matter. While scientists haven’t directly observed it before, dark matter produces unseen effects on objects in the universe, similar to those produced by visible matter.

Likewise, Roman will see exploding stars called supernovas that allow astronomers to measure how fast the universe has been expanding. Those measurements will help astronomers measure the varying expansion rate of the universe. Dark matter and dark energy, also not yet directly observed, together are the source of the vast majority of energy in the universe, but their physical natures are unknown.

Closer to home, Roman will monitor small variations in light from stars near the centre of the Milky Way galaxy in order to infer the presence of rogue planets drifting between the stars. As a planet passes in front of a star, it briefly perturbs and magnifies the light from the star behind it, confirming its presence.

Finally, Roman has a coronagraph instrument that will test the technology for future missions that plan to detect Earth-like planets around other stars. A coronagraph blocks most of the light from a star so astronomers can detect the much fainter planets orbiting it. For an Earth-like planet around a sun-like star, the host star can be 10bn times brighter than the planet.

The Roman telescope’s coronagraph will help it study distant planets as they pass in front of a star.

From spy telescope to surveying the universe

When NASA received Roman, it turned one challenge about the telescope’s spy design into an opportunity. The telescope has a wide field of view, at least compared with most space telescopes made for astronomy. This means it can see a large swath of the sky at once. There lies an opportunity – while telescopes like the Hubble Space Telescope see narrow fields very deeply, Roman will be able to see much larger fields.

Roman’s camera has such a wide field because its spy telescope origins give it unusually fast optics. This means that it has a relatively short focal length – the distance between the mirror and the point where the light focuses – for the diameter of its mirror. Effectively, it can project a much larger piece of sky onto a fixed area in the telescope, called the focal plane. Its mirror is about the same diameter as Hubble’s, yet it can capture an area about a hundred times larger per image.

Big science, big detectors

One modification that NASA made included building a large focal plane, the component of the telescope that collects light, made up of 18 wide area near-infrared detectors. These detectors are almost identical to those in the James Webb Space Telescope, but they have four times the number of pixels. They will take in infrared light, which has longer wavelengths than the light that human eyes can see. But while each one on Webb has four megapixels, or 4m pixels, Roman’s detectors have about 16 megapixels, bringing the full coverage of the 18 detectors to around 300 megapixels.

These detectors are modern marvels in their own right and represent the culmination of a long heritage of new technology that enables new discoveries.

I worked with colleagues in the early 2000s to advance the technology used in this type of detector – versions of which have been used on Hubble and Webb. We measured in the lab how the detectors performed in a simulated space environment. We needed to make sure that they could still sense even tiny signals out in space, which would allow the telescope to take in light from very faint planets, stars and galaxies.

We now see the fruits of those efforts in the beautiful images that Webb has produced, including of perplexing young galaxies in the early universe. I’m excited to see the images Roman will produce, using the newest iteration of this technology.

The Roman Space Telescope inside its payload fairing. Encapsulation shields the spacecraft during rollout, ascent, and the early phases of flight. Image: NASA/Sydney Rohde (Rocz)

New technology and a legacy of discovery

Astronomers are already eagerly awaiting the astronomical discoveries that Roman and its ultra-sensitive infrared detectors will produce. But how can we have such high expectations of a space observatory that has not even left the ground?

It is because technology precedes discovery. Roman’s detectors are the latest iteration in a long history of scientific success. You can directly tie countless Nobel Prizes to the telescope.

The team of physicists that inferred the existence of dark energy received a Nobel Prize in 2011. The observations they used came from new families of sensitive digital imaging detectors called charge-coupled devices, or CCDs, invented in the early 1970s. These devices helped astronomers measure how fast stars moved through space, which supported the notion that space is permeated with some unknown ‘dark’ matter.

Important validation of that Nobel-Prize-winning research came from near-infrared detectors that used the same technology as Roman.

Likewise, the Nobel Prize for the discovery of the supermassive black hole in the centre of the Milky Way galaxy was awarded to researchers who used a variety of infrared instruments on large ground-based telescopes.

All these telescopes had been outfitted with newly available infrared detectors. In fact, this Nobel Prize highlights the impact of three technologies: infrared detectors, big telescopes and adaptive optics.

Part of a continuing cycle

People often think of scientific discovery as a eureka light bulb above the head of a brilliant scientist, but discovery rarely happens that way. More often, someone uses a new technology to look at something that, until that point, remained unseen.

Galileo used a telescope to observe the previously unseen moons of Jupiter. Then, the next technological iteration replaced the human eye with photographic plates in the 19th century, which led to the first sensitive all-sky surveys. These surveys yielded a plethora of astronomical discoveries, including that the universe is expanding.

The 1970s saw electronic detectors take over for photographic plates, increasing the detector sensitivity by an order of magnitude. These gains were then transferred to infrared, rather than just visible light, opening a new window into the universe and another wave of discovery.

And now, it is Roman’s turn. But Roman won’t be on the cutting edge for long, because NASA is already designing the next space telescope.

The agency is developing the Habitable Worlds Observatory, a future space mission with the goal of directly imaging Earth-like planets around nearby stars and identifying signs of life in the universe.

Let’s hope for a smooth ride to space for Roman. In the meantime, scientists are already planning for the next generation of discoveries, one new detector at a time.

By Prof Don Figer

Don Figer is a professor in the College of Science and founder and director of the Rochester Imaging Detector Laboratory, Center for Detectors, and the Future Photon Initiative at Rochester Institute of Technology. He previously was co-founder and director of the Independent Detector Testing Laboratory at the Space Telescope Science Institute, where he led a team to do the competitive characterisation of prototype infrared detectors for the James Webb Space Telescope.

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

Maybe the best gift you can give your children is the experience of failure. It sounds radical, perhaps even a little uncomfortable, particularly when so much of parenting involves trying to protect children from frustration and disappointment. Yet by stepping in every time something becomes difficult, we may also be taking away opportunities for them to develop the very skills they will need to navigate an increasingly unpredictable world.

The question has become more pressing as artificial intelligence changes the way we think about work. In an earlier Trending Forward conversation, I explored what happens when AI begins taking over tasks and skills that once required years of training. The challenge isn’t simply predicting which jobs might disappear. It is figuring out how to prepare people for a labor market where the skills that are valuable today may not look quite so valuable a decade or two from now.

Children growing up today will enter that world without us knowing exactly what it will look like. Some of the jobs they eventually pursue may not exist yet. Two decades ago, working for a YouTube creator would hardly have been an obvious career aspiration; today, it is an entirely plausible profession. Rather than trying to guess the exact careers that will emerge, it makes more sense to focus on the human capabilities that can travel from one situation to another: curiosity, judgment, problem-solving and perseverance.

Those ideas were at the heart of my latest Trending Forward conversation with Rachele Harmuth, Chief Product Officer at CrunchLabs, the education and entertainment company founded by former NASA engineer and YouTube creator Mark Rober. I came into the interview already familiar with the company’s approach. Years ago, I bought a CrunchLabs Build Box subscription for one of my granddaughters and spent hours assembling projects with her. The projects were clever and genuinely educational, but what stood out was that they did not always go together perfectly on the first try.

Looking back, I suspect the failed attempts were at least as valuable as the finished projects.

AI changes what kids need to learn

For generations, education has placed considerable emphasis on accumulating information and mastering established skills. Children learn dates, formulas, languages, coding and other disciplines with the expectation that those abilities will help prepare them for work later in life. Knowledge still matters, but AI is making it increasingly easy for machines to perform many tasks that once depended on a person’s ability to recall information or execute a process correctly.

The more difficult question is what happens when there isn’t a clear process to follow.

Harmuth and I talked about the difficulty of predicting which careers will thrive 15 or 20 years from now. The answer isn’t simply to identify today’s most promising technical skill and teach it to every child. By the time today’s eight-year-olds enter the workforce, the technology around them will almost certainly have changed. The better investment may be in teaching them how to approach unfamiliar problems, work through uncertainty and keep going when the first solution doesn’t work.

CrunchLabs summarizes that philosophy in a simple phrase: “Think like an engineer.” It isn’t a suggestion that every child who builds one of its projects should eventually design bridges, develop robots or work for NASA. Engineering, in this context, is a mindset for solving problems: define what you are trying to accomplish, brainstorm possible solutions, test one, understand what went wrong and make an adjustment.

Curiosity, creative confidence and an ability to embrace failure sit at the center of that approach. The first two are relatively easy for adults to understand. The third is much harder.

Making failure less threatening

Parents and grandparents naturally want to fix things. When we watch a child struggle with a puzzle, an assembly step or a new skill, the instinct can be almost automatic: move that piece, turn it around, let me show you. Sometimes intervention is necessary, but removing every obstacle also removes the chance to discover what happens when you work through frustration yourself.

Harmuth made an interesting comparison during our conversation. Kids already understand this process when they play video games. Nobody expects to pick up a controller and beat a difficult game on the first attempt. You fail, restart, adjust your approach and try again. A failed attempt isn’t evidence that you are incapable of playing the game. In many ways, it is how you learn to play it.

The physical world often produces a very different reaction. A drawing doesn’t turn out as expected and a child decides they are bad at art. An instrument squeaks and suddenly they aren’t musical. A build fails and the conclusion becomes, “I can’t do this.” The problem isn’t the failed attempt itself; it is the meaning we attach to it.

Mark Rober’s work offers a useful counterexample. His videos showcase remarkable inventions, but the finished creation doesn’t tell the whole story. Behind the polished result are failed ideas, prototypes and iterations that didn’t work. The willingness to show those failures is part of what makes his approach to engineering accessible to children in the first place.

CrunchLabs applies the same philosophy across its products. Build Boxes introduce younger children to hands-on physics and engineering projects, while the Hack Pack gives older kids an opportunity to explore robotics. The company’s CrunchLabs Mysteries books take the idea into another format, turning reading into a hands-on STEM adventure and asking children to solve puzzles and build devices themselves. I recently looked at that expansion into storytelling, and it reflects the same underlying idea: STEM doesn’t have to mean sitting down and being told what to learn. It can involve figuring something out for yourself.

The company is also expanding into retail, summer camps and Class CrunchLabs, a nonprofit initiative offering free STEM curricula for grades 3 through 8. The formats are different, but the lesson remains remarkably consistent: try, fail, learn, adjust and try again.

Resilience Is a Teachable Skill

My favorite moment in our discussion was Harmuth’s perspective on resilience. We often treat resilience as an innate trait—you either have it or you don’t. Harmuth argues it is actually a muscle built from specific skills: problem-solving, perseverance and adaptability.

Parents can help build those skills without deliberately making childhood harder. A child working on a skateboard trick, learning an instrument, writing a story, experimenting in the kitchen or building a backyard fort is doing much of the same mental heavy lifting as a child working through an engineering project. Something doesn’t work, so they have to figure out why, make an adjustment and decide what to try next.

Curiosity gets a child wondering what might happen if they try something different. Creative confidence gives them the belief that they can figure it out. When the first attempt fails, resilience helps them move past “I can’t do this” and toward the much more useful question: “What should I try next?”

Those habits could become especially valuable as AI becomes better at generating answers. Humans will still need to decide which questions are worth asking, evaluate whether an answer makes sense and work out what to do when the problem in front of them doesn’t have an obvious solution.

Skills for a Future We Can’t Predict

None of us can say with any confidence which careers will thrive 15 or 20 years from now. Many of the jobs today’s children eventually pursue may not even exist yet. Two decades ago, the idea of working for a YouTube creator as a dream job would have sounded nonsensical. Today, it is an entirely plausible career path.

Preparing children for one particular version of the future may therefore be less useful than giving them skills they can carry into whatever that future looks like. Curiosity, judgment, problem-solving and perseverance aren’t tied to a particular profession. They can serve a child who becomes an engineer, an artist, a teacher or something we haven’t even thought to call a career yet.

“Think like an engineer” works as a philosophy precisely because it isn’t really about engineering. The process is simple enough: figure out what you are trying to accomplish, brainstorm possible solutions, test one, see what went wrong and adjust. A child doesn’t have to know anything about engineering to benefit from learning how to approach a problem this way.

Years after buying that CrunchLabs Build Box for my granddaughter, I think differently about the projects we built together. The satisfaction of getting something to work was certainly part of the fun, but the moments when we had to stop and figure out why something wasn’t working may have been more important. Getting the answer right was only part of the experience. Learning how to get there mattered too.

Parents naturally want to fix things for their children. We see frustration and want to make it disappear. Sometimes helping is exactly what a child needs; other times, giving them the space to struggle with a problem for a little longer may be the more valuable response.

The world today’s children inherit will be shaped by technologies we cannot fully anticipate, and the jobs they eventually do may look very different from the ones we know today. Preparing them for that uncertainty doesn’t necessarily mean teaching them every new technology as it arrives. It may mean giving them the confidence to face something unfamiliar, the resilience to fail without losing heart and the curiosity to keep experimenting.

My conversation with Rachele Harmuth left me with a simple thought: we may not be able to prepare our children for every problem they will encounter, but we can help them become better at working through problems. Even if they never become engineers, learning to think like one could prove to be one of the most useful things we give them.